This book highlights and explains the details of machine learning models used in geospatial data analysis. It demonstrates the need for a data-centric explainable machine learning approach for obtaining new insights from geospatial data analysis and how they are applied to solve various environmental problems from forestry to climate change.
This book highlights and explains the details of machine learning models used in geospatial data analysis. It demonstrates the need for a data-centric explainable machine learning approach for obtaining new insights from geospatial data analysis and how they are applied to solve various environmental problems from forestry to climate change.
Courage Kamusoko is an independent geospatial consultant based in Japan. His expertise includes land use and land cover change modelling, and the design and implementation of geospatial database management systems. He is focused on the analyses of remotely sensed images and machine learning. He teaches practical machine learning for geospatial data analysis and modelling at the University of Tsukuba, Japan. Dr. Kamusoko has authored and co-edited six books total. He has contributed many chapters in published books along with conference papers and proceedings.
Inhaltsangabe
Part I: Introduction. 1. Machine Learning for Geospatial Data Analysis: Challenges and Opportunities. Part II: Foundations. 2. An Introduction to Explainable Machine Learning. 3. Approaches to Explainable Machine Learning. 4. Approaches to Explainable Deep Learning. 5. Landslide Susceptibility Modeling using a Logistic Regression Model. Part III: Techniques and Applications. 6. Urban Land Cover Classification using Earth Observation (EO) Data and Machine Learning Models. 7. Modeling Forest Canopy Height using Earth Observation (EO) Data and Machine Learning Models. 8. Modeling Aboveground Biomass Density using Earth Observation (EO) Data and Machine Learning Models. 9. Explainable Deep Learning for Mapping Building Footprints using High-Resolution Imagery. 10. Towards Explainable AI and Data-centric Approaches for Geospatial Data Analysis. 11. Appendix.
Part I: Introduction. 1. Machine Learning for Geospatial Data Analysis: Challenges and Opportunities. Part II: Foundations. 2. An Introduction to Explainable Machine Learning. 3. Approaches to Explainable Machine Learning. 4. Approaches to Explainable Deep Learning. 5. Landslide Susceptibility Modeling using a Logistic Regression Model. Part III: Techniques and Applications. 6. Urban Land Cover Classification using Earth Observation (EO) Data and Machine Learning Models. 7. Modeling Forest Canopy Height using Earth Observation (EO) Data and Machine Learning Models. 8. Modeling Aboveground Biomass Density using Earth Observation (EO) Data and Machine Learning Models. 9. Explainable Deep Learning for Mapping Building Footprints using High-Resolution Imagery. 10. Towards Explainable AI and Data-centric Approaches for Geospatial Data Analysis. 11. Appendix.
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